REQUESTIONING OLD RUSSIAN AUTOBIOGRAPHICAL WRITINGS

More documents

Recommendations

Info

expectancy, we conducted research on macroeconomic determinants of life expectancy and on the level and structure of alcohol consumption. GDP per capita (in US dollars) or energy consumption per capita are usually used to assess standards of living (Brenner, 1975, 1979; Kossova, 1991; McAvinchey, 1994; Macela et al, 2001; Johansson et al, 2006). However, for cross-regional analysis, we recognized a need to find indicators reflecting more subtle differences in the level of welfare. This problem is particularly acute in Russia, where uneven regional development has become one of the most important migration factors. The most active part of the population (especially youth) migrates to more developed regions and regularly supports their relatives financially. This trend is not reflected in the population income data. In such circumstances, cost figures are more informative. One of the products indicating wealth is sugar. Sugar consumption has a positive correlation with standards of living. Analysis of data on the cost structure of the Russian population has shown that the category of indicative goods in Russia in additional to sugar may include computers and gold, as well as meat. Variation on these measures in the Russian regions allows us to evaluate the differences in wealth, so the per capita spending on these indicative goods could be used as a factor in our model. Since these products are not classified as essential, regions with higher spending on their purchase are considered well off in terms of disposable income. In addition to wealth indicators, we used common macroeconomic determinants, such as unemployment rate, urbanization rate, population density, proportion of the male population, and Gini index as independent variables in the models discussed below. Result of regression analysis on life expectancy (Russian regions) Table 2.8. Dependent variable: Life expectancy, N=81 Variable Coefficient Standard Deviation t-statistic Prob. Const 71.55501 0.702217 101.8988 0.0000 Sugar spending 8.66E-07 1.15E-07 7.558250 0.0000 Computers spending per capita 1.159962 0.425444 2.726476 0.0080 Gold spending per capita 0.640320 0.286236 2.237034 0.0284 Meat spending per capita -0.753829 0.156784 -4.808070 0.0000 Unemployment rate -18.94996 10.17685 -1.862065 0.0667 LOG(absolute alcohol consumption) -1.929813 0.264655 -7.291795 0.0000 Tiva Republic dummy variable -5.843734 1.604619 -3.641821 0.0005 Coefficient of determination R-squared 0.751823 F-statistic 31.15933 Prob(F-statistic) 0.000000 16
The coefficients of the model verify our hypothesis that there is a positive connection between a population’s life expectancy and its level of prosperity. Differences in wealth were assessed on the basis of spending on indicative goods. The negative impact of unemployment on life expectancy also came as no surprise. A dummy variable was introduced to eliminate the distorting influence of the Tuva Republic, where average life expectancy is significantly lower. An important implication of this model is the constant negative impact of the consumption of pure alcohol on average life expectancy in the regions. We noted that alcohol abuse not only reduces life expectancy, but also has a significant impact on mortality from external causes. This indicator has a strong negative correlation with life expectancy, with a coefficient of -0.84. Regions with the least mortality from external causes are usually included in the list of regions with the highest life expectancy, and vice versa. At the same time, variations in mortality from external causes are significantly higher. In order to test the hypothesis that the level of alcohol consumption has a negative impact on health in both the male and female populations, we have built regression models where the dependent variables were indicators of life expectancy for men and women separately. Table 2.9. Result of regression analysis on (male) life expectancy (Russian regions) Dependent variable: Life expectancy (male), N=80 Variable Coefficient Standard Deviation t-statistic Prob. Const 72.15743 1.005047 71.79509 0.0000 Computers spending per capita 0.784776 0.375159 2.091851 0.0400 Meat spending per capita -0.462243 0.138474 -3.338123 0.0013 Sugar spending 5.06E-07 1.13E-07 4.462103 0.0000 Unemployment rate 17.96891 6.266397 2.867503 0.0054 Mortality rate from external reasons -0.036893 0.003813 -9.675733 0.0000 Vodka consumption -0.147239 0.055514 -2.652285 0.0099 Beer to vodka consumption ratio -0.170841 0.080180 -2.130706 0.0366 Wine to vodka consumption ratio -2.725992 0.781676 -3.487367 0.0008 Coefficient of determination R-squared 0.858288 F-statistic 53.75181 Prob(F-statistic) 0.000000 Mortality from external causes, which allows us to separate the contribution of short-term alcohol effects on health and living standards from long-term (cumulative) effects, was introduced into this model as the independent variable. In this model, we also used data on consumption of alcoholic beverages by type to reflect the impact of the structure of alcohol consumption on average life expectancy. Converting these to ratios helped to avoid problems arising from multicollinearity in data on the consumption of different alcoholic beverages types. 17
Page 2 and 3: Kossova T.V. 1 , Kossova E.V. 2 , S
Page 4 and 5: in health during the economic downt
Page 6 and 7: Main variables used in this researc
Page 8 and 9: consider consuming more than 60 gra
Page 10 and 11: The average Russian consumes approx
Page 12 and 13: periods. The exception of 2009 is m
Page 14 and 15: Chukotka dummy variable -1.658234 0
Page 18 and 19: This raises the question as to whet
Page 20 and 21: e explained by the same factors tha
Page 22 and 23: 16. Johansson E., Bockerman P., Pra
Page 24 and 25: 48. Alcohol policy and public good.
Page 26 and 27: Annex 1. Absolute alcohol consumpti

REQUESTIONING OLD RUSSIAN AUTOBIOGRAPHICAL WRITINGS

Create successful ePaper yourself

Delete template?

Save as template?